Ecommercegan: A generative adversarial network for e-commerce

Kumar, A and Biswas, A and Sanyal, S (2018) Ecommercegan: A generative adversarial network for e-commerce. In: 6th International Conference on Learning Representations, ICLR 2018, 30-3 May 2018, Vancouver; Canada.

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Abstract

E-commerce companies such as Amazon, Alibaba and Flipkart process billions of orders every year. However, these orders represent only a small fraction of all plausible orders. Exploring the space of all plausible orders could help us better understand the relationships between the various entities in an e-commerce ecosystem, namely the customers and the products they purchase. In this paper, we propose a Generative Adversarial Network (GAN) for e-commerce orders. Our contributions include: (a) creating a dense and low-dimensional representation of e-commerce orders, (b) train an ecommerceGAN (ecGAN) with real orders to show the feasibility of the proposed paradigm, and (c) train an ecommerce-conditional-GAN (ec2GAN) to generate the plausible orders involving a particular product. We evaluate ecGAN qualitatively to demonstrate its effectiveness. The ec2GAN is used for various kinds of characterization of possible orders involving cold-start products.

Item Type:	Conference Paper
Publication:	6th International Conference on Learning Representations, ICLR 2018 - Workshop Track Proceedings
Publisher:	International Conference on Learning Representations, ICLR
Additional Information:	cited By 2; Conference of 6th International Conference on Learning Representations, ICLR 2018 ; Conference Date: 30 April 2018 Through 3 May 2018; Conference Code:149807
Keywords:	Adversarial networks; Cold start; E-commerce ecosystems; Low-dimensional representation, Electronic commerce
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	24 Sep 2020 11:07
Last Modified:	24 Sep 2020 11:07
URI:	http://eprints.iisc.ac.in/id/eprint/65405

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